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Efficient stochastic generators for high-resolution global climate simulations from CESM2-LENS2

Abstract Number:

2722 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yan Song (1), Zubair Khalid (2), Marc Genton (1)

Institutions:

(1) King Abdullah University of Science and Technology, N/A, (2) School of Science and Engineering, Lahore University of Management Sciences, Pakistan

Co-Author(s):

Zubair Khalid  
School of Science and Engineering, Lahore University of Management Sciences
Marc Genton  
King Abdullah University of Science and Technology

First Author:

Yan Song  
King Abdullah University of Science and Technology

Presenting Author:

Yan Song  
N/A

Abstract Text:

Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For the newly published CESM2-LENS2 data, which suffer from this issue, we propose a novel stochastic generator (SG) as a practical complement to the CESM2, capable of rapidly producing emulations closely mirroring training simulations. Our SG leverages the spherical harmonic transformation to shift from spatial to spectral domains, enabling efficient low-rank approximations that significantly reduce computational and storage costs. By accounting for axial symmetry and retaining distinct ranks for land and ocean regions, our SG captures intricate non-stationary spatial dependencies. Additionally, a modified Tukey g-and-h transformation accommodates non-Gaussianity in high-temporal-resolution data. We apply the proposed SG to generate emulations for surface temperature simulations from the CESM2-LENS2 data across various scales, marking the first attempt of reproducing daily data. These emulations are then meticulously validated against training simulations.

Keywords:

Emulator|Global temperature|Low-rank approximation|Non-stationary spatial structure|Tukey g-and-h transformation|

Sponsors:

Section on Statistics and the Environment

Tracks:

Spatio-temporal statistics

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